Regarding the lack of precision, because some functions are not easy to express in Garbled Circuit or the time efficiency caused by the expression is particularly poor, it needs to be handled by approximate polynomial functions. The computation of polynomials only includes addition and multiplication, so Secret Sharing can be competent, but there is still a gap between the approximate function and original function, resulting in a lack of accuracy in the results. Ultimately, a compromise is required between the model accuracy and time efficiency. Furthermore, because neural network requires high-precision float-point numbers but Secret Sharing is performed in the fixed-point number domain, it is necessary to convert float-point numbers into fixed-point numbers. In order to ensure the precision, we need transform the number by letting \({x^{\prime }} = {2^{{l_D}}}x\) , making the least most \({l_D}\)  bits of the fixed-point number represent the fractional part of the float-point number. For the further analysis on the lack of precision, please refer to SecureML[1]}. Because enough decimals can be retained, the lack of precision in this part can actually be ignored.
